big data

Big DataTopics Covered

•What is Big Data?•Big Data Laws•Why Big Data?•Industries using Big Data•Current process/SW in SCM •Challenges in SCM industry•How Big data can solve the problems? •Migration to Big data for an SCM industry

What is Big Data?

IBM SaysEvery day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.

WIKI SaysBig data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach. Until now, there was no practical way to harvest this opportunity. Today, IBM’s platform for big data uses state of the art technologies including patented advanced analytics to open the door to a world of possibilities.

What is Big Data?Real Time

What is Big Data?Changes & Challenges

Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".

The challenges include•capture •curation •storage •search •sharing •analysis •visualization

What is Big Data?The key platform capabilities include

Visualization and Discovery: Discover, understand, search, and navigate federated sources of big data while leaving that data in place.

Stream Computing: Continuously analyze massive volumes of streaming data with sub-millisecond response times to take action in real-time.

Text Analytics: Analyze textual content to uncover hidden meaning and insight in unstructured information.

Data Warehousing: Store and analyze large volumes of structured information with workload optimized systems designed for deep & operational analytics.

Hadoop-based Analytics: Store any data type in the low-cost, scalable Hadoop engine to lower the cost of processing and analyzing massive volumes of data.

What is Big Data?Supporting platform services

Accelerators: Faster time to value with pre-packaged analytical and industry-specific content.

Information Integration and Governance: Integrate, protect, cleanse, govern, and deliver your trusted information

Reference Architectures: Hardware, networking and system software blueprints to accelerate time to value.

Systems Management: Monitor and manage your big data system for secure and optimized performance.

Application Development: Streamline the process of developing big data applications.

What is Big Data?

What is Big Data?Examples

Examples include Big Science, web logs, RFID, sensor networks, social networks, social data (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, medical records, photography archives, video archives, and large-scale e-commerce.

Big Data Laws

Why Big Data?

Industries using Big Data

•Banking•Risk and fraud management•Customer analytics•Transportation•Logistics optimization•Traffic congestion•Healthcare•Medical record text analytics•Genomic analytics•Telecommunications•Call detail record processing•Customer profile monetization

•Energy and Utilities•Smart meter analytics•Asset management•Digital Media•Real-time ad targeting•Website analysis•Retail•Omni-channel marketing•Click-stream analysis•Government•Threat prediction and prevention•Fraud and abuse management

Industries using Big Data

Big data = Big Return on Investment (ROI)While there is a lot of buzz about big data in the market, it isn’t hype.

Healthcare: 20% decrease in patient mortality by analyzing streaming patient data

Telco: 92% decrease in processing time by analyzing networking and call data

Utilities: 99% improved accuracy in placing power generation resources by analyzing 2.8 petabytes of untapped data

Current process/SW in SCM

•ERP and SCP • WMS•TMS•MES

Current process/SW in SCM overcomes

Lack of visibility: Large corporations were left with too much inventory when the recession hit and too little when demand picked up in 2009. “Users are looking at applications like sales and operations planning, transportation management and asset management applications that can be leveraged to track goods in motion,” Eschinger says.

Enabling corporate strategy: Everyone wants to reduce costs, but increasingly businesses are targeting supply chains to improve overall corporate viability, especially customer service.

Total landed cost: Blame high transportation costs, increasing wages in emerging markets and multi-channel sales and distribution strategies, but companies are taking a more analytical look at what it costs to fill an online order versus a store and what is the total cost to source in Mexico versus China.

Current process/SW in SCM

Challenges in SCM industry


Customer serviceEffective supply chain management is all about delivering the right product in the right quantity and in the right condition with the right documentation to the right place at the right time at the right price. If only it were as simple as it sounds.

Cost controlSupply chain operating costs are under pressure today from rising freight prices, more global customers, technology upgrades, rising labor rates, expanding healthcare costs, new regulatory demands and rising commodity prices. To control such costs there are thousands of potential metrics that supply chain organizations can and do measure. Managers need to zero in on the critical few that drive total supply chain costs within their organizations.

Planning and Risk ManagementSupply chains must periodically be assessed and redesigned in response to market changes, including new product launches, global sourcing, new acquisitions, credit availability, the need to protect intellectual property, and the ability to maintain asset and shipment security. In addition, supply chain risks must be identified and quantified. SCC members report that less than half of their organizations have metrics and procedures for assessing, controlling, and mitigating such risks.

TalentAs experienced supply chain managers retire, and organizations scale up to meet growing demand in developing markets, talent acquisition, training, and development is becoming increasingly important. Supply chain leaders need a thorough understanding of the key competencies required for supply chain management roles, specific job qualifications, methods for developing future talent and leaders, and the ability to efficiently source specific skill sets.

Supplier/partner relationship managementDifferent organizations, even different departments within the same organization, can have different methods for measuring and communicating performance expectations and results. Trust begins when managers let go of internal biases and make a conscious choice to follow mutually agreed upon standards to better understand current performance and opportunities for improvement.

How Big data can solve the problems?


Digital Path to Purchase. The work that McCormick is doing on Digital Path to Purchase is breakthrough thinking. I would start there.

ecommerce. Amazon‘s work on understanding demand insights of pantry shopping is exciting. They are an early leader in Big Data techniques. I would have them at the top of my list.

Listening. Text mining and ratings and review information at Bazaarvoice is a Big Data service. How could companies use this data? How could it help in sensing early product failure? Only one out of ten companies that I talk to have ever heard of Bazaarvoice and the great work that they are doing.

Safe and Secure Supply Chains. The work at Eli Lilly on product serialization of pharmaceuticals has direct applicability of where we are headed on food safety. Food and beverage companies need to learn from this early work in Big Pharma..


Supply Chain Visibility. Let’s face it, we have been talking about supply chain visibility and agile supply chains for many years, but it has just been talk. The use of rules-based ontologies and learning systems to redefine supply chain visibility at Conair is a new way to think about sensing supply chains. While early, it is a great case study on how to use Big Data techniques to solve a tough problem.

Supplier Sensing. The work at D&B on supplier sensing is a great use of Big Data. I would include them in the list and work with them on how consumer products companies and retailers can sense supplier failure early and use it to build stronger supplier relationships. We have talked about collaboration, but in reality, we have pushed costs and working capital back into the chain increasing risk. The further back in the supply chain that we go and sense supplier health, the weaker the players and the greater the risk to the brand. The work at D&B is a great start to better understand this.

Large-scale ERP. The race is on for global companies to better serve emerging markets. These markets are fraught with disparate data that is often incomplete. These large consumer products companies are also the companies with BIG DATA ERP systems. What are best practices for companies in the ERP Petabyte club? How does the data change? How does it affect maintenance and upgrade cycles? And the definition of business analytics?

Demand Insights. The work at Kraft on consumer insights, or the work at General Mills on downstream data are Big Data problems in the making. While both companies today are using more conventional techniques, the size of the data is growing and insights can be gained on where we are headed.

Migration to Big data for an SCM industry

The End

Thanks

Prepared byRedwanul Haq Choyon+8801611-222447email: [email protected]

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